import os
import sys
import numpy as np
import tensorflow as tf
import matplotlib
import matplotlib.pyplot as plt
import keras

# Root directory of the project
ROOT_DIR = os.path.abspath("../../")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
from mrcnn.model import log

%matplotlib inline 

# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

# Local path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")
# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)

# Path to Shapes trained weights
SHAPES_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_shapes.h5")

# Show stats of all trainable weights    
visualize.display_weight_stats(model)

# Pick layer types to display
LAYER_TYPES = ['Conv2D', 'Dense', 'Conv2DTranspose']
# Get layers
layers = model.get_trainable_layers()
layers = list(filter(lambda l: l.__class__.__name__ in LAYER_TYPES, 
                layers))
# Display Histograms
fig, ax = plt.subplots(len(layers), 2, figsize=(10, 3*len(layers)),
                       gridspec_kw={"hspace":1})
for l, layer in enumerate(layers):
    weights = layer.get_weights()
    for w, weight in enumerate(weights):
        tensor = layer.weights[w]
        ax[l, w].set_title(tensor.name)
        _ = ax[l, w].hist(weight[w].flatten(), 50)